scholarly journals Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2338
Author(s):  
Sofia Agostinelli ◽  
Fabrizio Cumo ◽  
Giambattista Guidi ◽  
Claudio Tomazzoli
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Energy Management ◽  
Three Dimensional ◽  
Cost Effective ◽  
Automation System ◽  
Climate Conditions ◽  
Digital Twin ◽  
Building Energy Management ◽  
Computing Paradigm

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

scholarly journals BreastGAN: Artificial Intelligence-Enabled Breast Augmentation Simulation

2021 ◽  
Author(s):  
Christian Chartier ◽  
Ayden Watt ◽  
Owen Lin ◽  
Akash Chandawarkar ◽  
James Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Breast Augmentation ◽  
Three Dimensional ◽  
Cost Effective ◽  
Senior Author ◽  
Surgical Results ◽  
Clinical Images ◽  
Before And After ◽  
Image Pairs

Abstract Background Managing patient expectations is important to ensuring patient satisfaction in aesthetic medicine. To this end, computer technology developed to photograph, digitize, and manipulate three-dimensional (3D) objects has been applied to the female breast. However, the systems remain complex, physically cumbersome, and extremely expensive. Objectives The authors of the current study wish to introduce the plastic surgery community to BreastGAN, a portable, artificial intelligence-equipped tool trained on real clinical images to simulate breast augmentation outcomes. Methods Charts of all patients who underwent bilateral breast augmentation performed by the senior author were retrieved and analyzed. Frontal before and after images were collected from each patient’s chart, cropped in a standardized fashion, and used to train a neural network designed to manipulate before images to simulate a surgical result. AI-generated frontal after images were then compared to the real surgical results. Results Standardizing the evaluation of surgical results is a timeless challenge which persists in the context of AI-synthesized after images. In this study, AI-generated images were comparable to real surgical results. Conclusions This study features a portable, cost-effective neural network trained on real clinical images and designed to simulate surgical results following bilateral breast augmentation. Tools trained on a larger dataset of standardized surgical image pairs will be the subject of future studies.

scholarly journals Assessing Crop Water Requirements and a Case for Renewable-Energy-Powered Pumping System for Wheat, Cotton, and Sorghum Crops in Sudan

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8133
Author(s):  
Zafar A. Khan ◽  
Muhammad Imran ◽  
Jamal Umer ◽  
Saeed Ahmed ◽  
Ogheneruona E. Diemuodeke ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Crop Yields ◽  
Renewable Energy Sources ◽  
Cost Effective ◽  
Electrical Energy ◽  
Crop Water ◽  
Water Shortages ◽  
Water Requirements ◽  
Climate Conditions ◽  
Crop Water Requirements

Climate change is changing global weather patterns, with an increase in droughts expected to impact crop yields due to water scarcity. Crops can be provided with water via underground pumping systems to mitigate water shortages. However, the energy required to pump water tends to be expensive and hazardous to the environment. This paper explores different sites in Sudan to assess the crop water requirements as the first stage of developing renewable energy sources based on water pumping systems. The crop water requirements are calculated for different crops using the CROPWAT and CLIMWAT simulation tools from the Food and Agriculture Organization (FAO) of the United Nations. Further, the crop water requirements are translated into electrical energy requirements. Accurate calculations of the energy needed will help in developing cost-effective energy systems that can help in improving yields and reducing carbon emissions. The results suggest that the northern regions tend to have higher energy demands and that the potential for renewable energy should be explored in these regions, which are more susceptible to drought and where crops tend to be under higher stress due to adverse climate conditions.

scholarly journals A QR code based framework for auto-configuration of IoT sensor networks in buildings

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Simon Soele Madsen ◽  
Athila Quaresma Santos ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Sensor Networks ◽  
Energy Management ◽  
Cost Effective ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Communication Signal ◽  
Management Capabilities ◽  
Global Carbon

AbstractWorldwide buildings are responsible for about 40% of the overall consumption and contribute to an average of 30% percent of the global carbon emissions. Nevertheless, most current buildings lack efficient energy management systems because such solutions are very expensive, especially when necessary instrumentation needs to be installed after the building’s construction. As an alternative, we purpose the use of IoT sensor networks to retrofit existing medium and large-sized buildings to provide energy management capabilities in a cost-effective way. An IoT network auto-configuration platform for building energy management was developed. In order to efficiently manage metadata related to location and devices, a database using dynamic QR codes was created. Furthermore, we discuss the potential and shortcomings of different sensor-gateway pairing strategies that are applicable to an auto-configuring system. Lastly, we share our implementation of these concepts and demonstrate their use in a medium-sized building case study. The results show a trade-off between optimal configuration and total configuration time with a focus on the quality of the communication signal strength. The proposal provided the necessary automation for a cost-effective energy management system that can be deployed in both new constructions and existing buildings.

scholarly journals Towards a Digital Twin model for Building Energy Management: Case of Morocco

2021 ◽  
Vol 184 ◽  
pp. 404-410
Author(s):  
Abdelali Agouzoul ◽  
Mohamed Tabaa ◽  
Badr Chegari ◽  
Emmanuel Simeu ◽  
Abbas Dandache ◽  
...  
Keyword(s):  
Energy Management ◽  
Building Energy ◽  
Digital Twin ◽  
Twin Model ◽  
Building Energy Management

scholarly journals Energy Management Model for HVAC Control Supported by Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8210
Author(s):  
Pedro Macieira ◽  
Luis Gomes ◽  
Zita Vale
Keyword(s):  
Predictive Model ◽  
Energy Management ◽  
Decision Model ◽  
Cost Effective ◽  
Building Energy ◽  
Office Buildings ◽  
Energy Prices ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Current Energy

Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method applied in this paper divides the addressed problem into three steps: (i) the continuous acquisition of data provided by an open-source building energy management systems, (ii) the proposed learning and predictive model able to predict if users will be working in a given location, and (iii) the proposed decision model to manage the HVAC units according to the prediction of users, current environmental context, and current energy prices. The results show that the proposed predictive model was able to achieve a 93.8% accuracy and that the proposed decision tree enabled the maintenance of users’ comfort. The results demonstrate that the proposed solution is able to run in real-time in a real office building, making it a possible solution for smart buildings.

scholarly journals Evaluation of renewable energy deployment scenarios for building energy management

AIMS Energy ◽  
2016 ◽  
Vol 4 (5) ◽  
pp. 742-761 ◽  
Author(s):  
Hossam A. Gabbar ◽  
◽  
Ahmed Eldessouky ◽  
Jason Runge
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Building Energy ◽  
Building Energy Management

scholarly journals Energy Management-Based Predictive Controller for a Smart Building Powered by Renewable Energy

2020 ◽  
Vol 12 (10) ◽  
pp. 4264 ◽  
Author(s):  
Younès Dagdougui ◽  
Ahmed Ouammi ◽  
Rachid Benchrifa
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Residential Building ◽  
Centralized Control ◽  
Control Approach ◽  
Electrical Grid ◽  
Smart Building ◽  
Building Energy Management ◽  
High Level ◽  
Horizon Planning

This paper presents a smart building energy management system (BEMS), which is in charge of optimally controlling the sustainable operation of a building-integrated-microgrid (BIM). The main objective is to develop an advanced high-level centralized control approach-based model predictive control (MPC) considering variations of renewable sources and loads. A finite-horizon planning optimization problem is developed to control the operation of the BIM. The model can be implemented as a BEMS for the BIM to manipulate the indoor temperature and optimize the operation of the system’s units. A centralized MPC-based algorithm is implemented for the power management scheduling of all sub-systems as well as power exchanges with the electrical grid. The MPC algorithm is verified over case studies applied to two floors residential building considering the climate condition of a typical day of March, where the effects of both loads and thermal resistance of building shell on the operation of the BIM are analyzed via numerical simulations. The analysis shows that 96% of the total electrical load has been fulfilled by the local production where 23% represents the total electric output of the micro-CHP and 73% is the renewable energy production. The deficit, which represents only 4%, is purchased from the electrical distribution network (EDN).

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